{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**异常检测**\n",
    "- 1、基于server-operational-params.csv 数据，可视化数据分布情况，及其对应高斯分布的概率密度函数\n",
    "- 2、建立模型、实现异常数据检测\n",
    "- 3、可视化异常检测处理结果\n",
    "- 4、修改概率分布预支EllipticEnvelope(contamination=0.1)中的 contamination参数，查看阈值改变对结果的影响"
   ],
   "id": "34f0ed1a40a7da82"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-21T10:04:09.311100Z",
     "start_time": "2025-06-21T10:04:08.840141Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "data = pd.read_csv('../data/server-operational-params.csv')\n",
    "data.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Latency (ms)  Throughput (mb/s)  Anomaly\n",
       "0     13.046815          14.741152        0\n",
       "1     13.408520          13.763270        0\n",
       "2     14.195915          15.853181        0\n",
       "3     14.914701          16.174260        0\n",
       "4     13.576700          14.042849        0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latency (ms)</th>\n",
       "      <th>Throughput (mb/s)</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.046815</td>\n",
       "      <td>14.741152</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.408520</td>\n",
       "      <td>13.763270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.195915</td>\n",
       "      <td>15.853181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.914701</td>\n",
       "      <td>16.174260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.576700</td>\n",
       "      <td>14.042849</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:06:22.279864Z",
     "start_time": "2025-06-21T10:06:22.088814Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure(figsize=(10, 5))\n",
    "plt.scatter(data.loc[:, 'Latency (ms)'], data.loc[:, 'Throughput (mb/s)'])\n",
    "plt.title('Latency (ms) vs Throughput (MB/s)')\n",
    "plt.xlabel('Latency (ms)')\n",
    "plt.ylabel('Throughput (MB/s)')\n",
    "plt.show()"
   ],
   "id": "66c20afb910e8224",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:10:38.013380Z",
     "start_time": "2025-06-21T10:10:38.004087Z"
    }
   },
   "cell_type": "code",
   "source": [
    "x1 = data.loc[:, 'Latency (ms)']\n",
    "x2 = data.loc[:, 'Throughput (mb/s)']"
   ],
   "id": "315797d12ae6f5bb",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:10:39.851249Z",
     "start_time": "2025-06-21T10:10:39.198228Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig2 = plt.figure(figsize=(20, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(x1, bins=100)\n",
    "plt.title('Latency (ms)')\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('counts')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(x2, bins=100)\n",
    "plt.title('Throughput (MB/s)')\n",
    "plt.xlabel('x2')\n",
    "plt.ylabel('counts')\n",
    "plt.show()"
   ],
   "id": "62833be5c8374922",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:11:37.558523Z",
     "start_time": "2025-06-21T10:11:37.552776Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 获取 均值 和 标准差\n",
    "x1_mean = x1.mean()\n",
    "x1_sigma = x1.std()\n",
    "x2_mean = x2.mean()\n",
    "x2_sigma = x2.std()\n",
    "print('x1_mean:', x1_mean, 'x1_sigma:', x1_sigma, 'x2_mean:', x2_mean, 'x2_sigma:', x2_sigma)"
   ],
   "id": "383c97e3f49abdf1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1_mean: 14.112225783945592 x1_sigma: 1.355957375820451 x2_mean: 14.99771050813621 x2_sigma: 1.309707117591831\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:13:39.778248Z",
     "start_time": "2025-06-21T10:13:39.189Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from scipy.stats import norm\n",
    "x1_range = np.linspace(0, 20 ,300)\n",
    "x1_normal = norm.pdf(x1_range, x1_mean, x1_sigma)\n",
    "x2_range =  np.linspace(0, 20 ,300)\n",
    "x2_normal = norm.pdf(x2_range, x2_mean, x2_sigma)"
   ],
   "id": "6da03e5a2e460580",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:14:33.380887Z",
     "start_time": "2025-06-21T10:14:33.048175Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig3 = plt.figure(figsize=(20, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x1_range, x1_normal)\n",
    "plt.title('normal p(x1)')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x2_range, x2_normal)\n",
    "plt.title('normal p(x2)')\n",
    "plt.show()"
   ],
   "id": "d2eb1466ba29a079",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:15:26.323708Z",
     "start_time": "2025-06-21T10:15:26.067481Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.covariance import EllipticEnvelope\n",
    "ad_model = EllipticEnvelope()\n",
    "ad_model.fit(data)"
   ],
   "id": "99ca2b212bd5708a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EllipticEnvelope()"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url();\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>EllipticEnvelope()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>EllipticEnvelope</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.covariance.EllipticEnvelope.html\">?<span>Documentation for EllipticEnvelope</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('store_precision',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">store_precision&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('assume_centered',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">assume_centered&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('support_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">support_fraction&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('contamination',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">contamination&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:16:58.701687Z",
     "start_time": "2025-06-21T10:16:58.690285Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y_predict = ad_model.predict(data)\n",
    "print(pd.Series(y_predict).value_counts())"
   ],
   "id": "4baf8aca7949a0ae",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1    276\n",
      "-1     31\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:21:51.315392Z",
     "start_time": "2025-06-21T10:21:49.486279Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# visualize the result\n",
    "fig4 = plt.figure(figsize=(12, 6))\n",
    "original_data = plt.scatter(data.loc[:, 'Latency (ms)'], data.loc[:, 'Throughput (mb/s)'],marker='x')\n",
    "anomaly_data = plt.scatter(data.loc[:, 'Latency (ms)'][y_predict==-1], data.loc[:, 'Throughput (mb/s)'][y_predict==-1],marker='o',facecolor='none',edgecolor='red',s=150)\n",
    "\n",
    "plt.title('anomaly detection result')\n",
    "plt.xlabel('Latency (ms)')\n",
    "plt.ylabel('Throughput (MB/s)')\n",
    "plt.legend((original_data, anomaly_data), ('normal', 'anomaly'))\n",
    "plt.show()"
   ],
   "id": "aeb52e51886633c1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-21T10:27:28.872959Z",
     "start_time": "2025-06-21T10:27:28.645796Z"
    }
   },
   "cell_type": "code",
   "source": [
    "ad_model = EllipticEnvelope(contamination=0.02)\n",
    "ad_model.fit(data)\n",
    "\n",
    "y_predict = ad_model.predict(data)\n",
    "\n",
    "fig5 = plt.figure(figsize=(12, 6))\n",
    "original_data = plt.scatter(data.loc[:, 'Latency (ms)'], data.loc[:, 'Throughput (mb/s)'],marker='x')\n",
    "anomaly_data = plt.scatter(data.loc[:, 'Latency (ms)'][y_predict==-1], data.loc[:, 'Throughput (mb/s)'][y_predict==-1],marker='o',facecolor='none',edgecolor='red',s=150)\n",
    "\n",
    "plt.title('anomaly detection result')\n",
    "plt.xlabel('Latency (ms)')\n",
    "plt.ylabel('Throughput (MB/s)')\n",
    "plt.legend((original_data, anomaly_data), ('normal', 'anomaly'))\n",
    "plt.show()"
   ],
   "id": "b10ccd7cd027a522",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**异常检测实战summary**\n",
    "- 1、通过计算数据各维度对应的高斯分布概率密度函数，可用于寻找到数据中的异常点\n",
    "- 2、通过修改概率密度阈值contamination,可调整异常点检测的灵敏度\n",
    "- sklearn.covariance.EmpiricalCovariance"
   ],
   "id": "d0e837cfcb1e7576"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": ".venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
